D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 69 Citations 32,019 453 World Ranking 878 National Ranking 528

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Magnetic resonance imaging
  • Statistics

Artificial intelligence, Computer vision, Magnetic resonance imaging, Segmentation and Image processing are his primary areas of study. Jerry L. Prince has included themes like Brain mapping and Pattern recognition in his Artificial intelligence study. His Computer vision study combines topics from a wide range of disciplines, such as Algorithm, Cortical surface and Atlas.

As a part of the same scientific study, he usually deals with the Magnetic resonance imaging, concentrating on Nuclear medicine and frequently concerns with Mr imaging. His research on Segmentation also deals with topics like

  • Cerebral cortex and related Central nervous system,
  • Fuzzy logic that connect with fields like Surface. His work in Image processing tackles topics such as Initialization which are related to areas like Vector flow.

His most cited work include:

  • Snakes, shapes, and gradient vector flow (3685 citations)
  • Current methods in medical image segmentation. (1653 citations)
  • Adaptive fuzzy segmentation of magnetic resonance images (732 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Artificial intelligence, Computer vision, Magnetic resonance imaging, Segmentation and Pattern recognition. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Imaging phantom. His Computer vision study incorporates themes from Tongue and Diffusion MRI.

The various areas that he examines in his Magnetic resonance imaging study include Image resolution, Biomedical engineering, Nuclear magnetic resonance and Superresolution. He has researched Segmentation in several fields, including Optical coherence tomography and Medical imaging. His work carried out in the field of Pattern recognition brings together such families of science as Deep learning and Data mining.

He most often published in these fields:

  • Artificial intelligence (57.91%)
  • Computer vision (40.54%)
  • Magnetic resonance imaging (24.44%)

What were the highlights of his more recent work (between 2017-2022)?

  • Artificial intelligence (57.91%)
  • Pattern recognition (16.53%)
  • Segmentation (22.03%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Pattern recognition, Segmentation, Magnetic resonance imaging and Deep learning are his primary areas of study. His Artificial intelligence study combines topics from a wide range of disciplines, such as Optical coherence tomography and Computer vision. His Computer vision study frequently draws connections between related disciplines such as Signal.

His Pattern recognition research is multidisciplinary, incorporating perspectives in Image processing, Normalization and Consistency. Specifically, his work in Segmentation is concerned with the study of Image segmentation. His research in Magnetic resonance imaging intersects with topics in Tongue, Interpolation, Resolution, Superresolution and Normal pressure hydrocephalus.

Between 2017 and 2022, his most popular works were:

  • Cross-modality image synthesis from unpaired data using CycleGAN effects of gradient consistency loss and training data size (68 citations)
  • Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images (38 citations)
  • Unpaired Brain MR-to-CT Synthesis Using a Structure-Constrained CycleGAN (33 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Internal medicine

Jerry L. Prince spends much of his time researching Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Magnetic resonance imaging. Jerry L. Prince combines subjects such as Healthy subjects, Dice, Position and Computer vision with his study of Artificial intelligence. His studies in Computer vision integrate themes in fields like Tongue, Resolution and Atlas.

His Pattern recognition research also works with subjects such as

  • Image quality together with Consistency and Scanner,
  • Digital image processing, Speckle noise and Image processing most often made with reference to Artificial neural network. His Segmentation study combines topics in areas such as Image synthesis, Image, Optical coherence tomography, Retinal and White matter lesion. The study incorporates disciplines such as Hydrocephalus, Ventricular system, Superresolution and Normal pressure hydrocephalus in addition to Magnetic resonance imaging.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Snakes, shapes, and gradient vector flow

Chenyang Xu;J.L. Prince.
IEEE Transactions on Image Processing (1998)

5981 Citations

Current methods in medical image segmentation.

Dzung L. Pham;Chenyang Xu;Jerry L. Prince.
Annual Review of Biomedical Engineering (2000)

3232 Citations

Gradient vector flow: a new external force for snakes

Chenyang Xu;J.L. Prince.
computer vision and pattern recognition (1997)

1369 Citations

Adaptive fuzzy segmentation of magnetic resonance images

D.L. Pham;J.L. Prince.
IEEE Transactions on Medical Imaging (1999)

999 Citations

Generalized gradient vector flow external forces for active contours

Chenyang Xu;Jerry L. Prince.
Signal Processing (1998)

991 Citations

Cardiac motion tracking using cine harmonic phase (harp) magnetic resonance imaging

Jerry L. Prince;Nael F. Osman.
Magnetic Resonance in Medicine (2000)

777 Citations

Measurement of radiotracer concentration in brain gray matter using positron emission tomography: MRI-based correction for partial volume effects.

Hans W. Müller-Gärtner;Jonathan M. Links;Jerry L. Prince;R. Nick Bryan.
Journal of Cerebral Blood Flow and Metabolism (1992)

721 Citations

An adaptive fuzzy C -means algorithm for image segmentation in the presence of intensity inhomogeneities

Dzung L. Pham;Jerry L. Prince.
Pattern Recognition Letters (1999)

669 Citations

Medical Imaging Signals and Systems

Jerry L Prince;Jonathan M Links.
(2005)

662 Citations

A Survey of Current Methods in Medical Image Segmentation

Dzung L. Pham;Chenyang Xu;Jerry L. Prince.
(1999)

641 Citations

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